Setup

library(SeqArray)
library(SNPRelate)
library(pander)
library(scales)
library(magrittr)
library(tidyverse)
library(readxl)
library(sp)
library(ggmap)
library(rgdal)
library(ggsn)
library(parallel)
library(qqman)
library(ggrepel)
library(plyranges)
library(rtracklayer)
theme_set(theme_bw())
panderOptions("missing", "")
mc <- min(12, detectCores() - 1)
alpha <- 0.05
w <- 169/25.4
h <- 169/25.4

This workflow immediately follows 03_SNPFiltering and performs an analysis on the SNPs retained after filtering. Analysis performed is:

gdsPath <- file.path("..", "5_stacks", "gds", "populations.snps.gds")
gdsFile <- seqOpen(gdsPath, readonly = TRUE)
keepSNPs <- readRDS("keepSNPsAfterLDPruning.RDS")
sampleID <- tibble(
    Sample = seqGetData(gdsFile, "sample.annotation/Sample"),
    Population = seqGetData(gdsFile, "sample.annotation/Population"),
    Location = seqGetData(gdsFile, "sample.annotation/Location")
)
popSizes <- sampleID %>% 
    group_by(Population) %>%
    summarise(n = dplyr::n())
snps <- tibble(
    variant.id = seqGetData(gdsFile, "variant.id") ,
    chromosome = seqGetData(gdsFile, "chromosome"),
    position = seqGetData(gdsFile, "position"),
    snpID = seqGetData(gdsFile, "annotation/id")
) %>%
    mutate(snpID = str_remove(snpID, ":.$"),
           snpID = str_replace(snpID, ":", "_")) %>%
    separate(snpID, into = c("Locus ID", "Col"), remove = FALSE) %>%
    mutate_at(vars(`Locus ID`, Col), as.integer) %>%
    mutate(Col = Col - 1,
           snpID = paste(`Locus ID`, Col, sep = "_")) %>%
    right_join(keepSNPs)
seqSetFilter(gdsFile, variant.id = snps$variant.id)
## # of selected variants: 18,886
genotypes <- snps %>%
    cbind(seqGetData(gdsFile, "genotype") %>% 
              apply(MARGIN = 3, colSums) %>%
              t %>%
              set_colnames(sampleID$Sample)) %>%
    as_tibble() %>%
    gather(Sample, Genotype, one_of(sampleID$Sample)) %>%
    dplyr::filter(!is.na(Genotype)) %>%
    arrange(variant.id, Sample) %>%
    left_join(sampleID)
seqResetFilter(gdsFile)
## # of selected samples: 146
## # of selected variants: 146,763
snpIn1996 <- genotypes %>% 
    filter(Population == 1996) %>% 
    group_by(variant.id) %>% 
    summarise(maf = mean(Genotype) / 2) %>%
    filter(maf > 0)
genotypes %<>% 
    right_join(snpIn1996)
seqClose(gdsFile)

In addition to the above, the set of genes corresponding the Ensembl Release 84 were loaded.

ensGenes <- file.path("..", "external", "Oryctolagus_cuniculus.OryCun2.0.96.gff3.gz") %>%
    import.gff3(feature.type = "gene", sequenceRegionsAsSeqinfo = TRUE) %>%
    .[,c("gene_id", "Name", "description")]

The set of SNPs under invetigation was also defined as a GRanges object.

snpsGR <- makeGRangesFromDataFrame(
    df = snps, 
    keep.extra.columns = TRUE, 
    ignore.strand = TRUE, 
    seqinfo = seqinfo(ensGenes), 
    seqnames.field = "chromosome", 
    start.field = "position", 
    end.field = "position"
)

This was intersected with the set of genes to connect SNPs to genes within 40kb.

snp2Gene <- snpsGR %>% 
    resize(width = 199999) %>%
    trim() %>%
    join_overlap_inner(ensGenes) 

PCA

lowCall <- c("gc2901", "gc2776", "gc2731", "gc2727", "gc2686")
snp4PCA <- genotypes %>% 
    filter(!Sample %in% lowCall) %>%
    group_by(variant.id, Population) %>% 
    summarise(n = dplyr::n()) %>% 
    spread(Population, n) %>%
    mutate(N = `1996` + `2010` + `2012`) %>% 
    ungroup() %>%
    filter(N > 0.95*(sum(popSizes$n) - length(lowCall)))
pca <- genotypes %>%
    filter(variant.id %in% snp4PCA$variant.id) %>%
    dplyr::select(variant.id, Sample, Genotype) %>%
    spread(Sample, Genotype) %>%
    as.data.frame() %>%
    column_to_rownames("variant.id") %>%
    as.matrix() %>%
    apply(2, function(x){
        x[is.na(x)] <- mean(x, na.rm = TRUE)
        x
    }) %>%
    t() %>%
    .[, apply(., 2, function(x){length(unique(x)) > 1})] %>%
    prcomp( center = TRUE)

As noted in the previous section sample samples gc2901, gc2776, gc2731, gc2727 and gc2686 had a SNP identification rate \(< 50\)% and as such these were markd as potential outliers. Ignoring these samples, and restricting data to SNPs identified in \(>95\)% of all samples, a preliminary PCA was performed This amounted to 7,767 of the possible 18,878 SNPs for analysis using PCA. Missing values were specified as the mean MAF over all populations combined.

Given the initially observed structure, in which samples from the 2012 population are separating from the other samples which group with the 1996 population, the collection points for the 2012 samples as investigated.

sampleID %<>%
    left_join(
        file.path("..", "external", "GPS_Locations.xlsx") %>%
            read_excel() %>%
            dplyr::select(Sample, ends_with("tude")) %>%
            mutate(Sample = gsub("[Oo][Rr][Aa] ([0-9ABC]*)", "ora\\1", Sample))
    )
*PCA showing population structure. Point size reflects the proportion of SNPs for which imputation was required, and the observed structure appeared independent of this.*

PCA showing population structure. Point size reflects the proportion of SNPs for which imputation was required, and the observed structure appeared independent of this.

loc <- c(range(sampleID$Longitude, na.rm = TRUE) %>% mean,
         range(sampleID$Latitude, na.rm = TRUE) %>% mean)
saPoly <- readRDS(file.path("..", "external", "saPoly.RDS"))
roads <- readRDS(file.path("..", "external", "roads.RDS"))
gc <- SpatialPoints(cbind(x = 138.655972, y = -31.200305))
proj4string(gc) <- "+proj=longlat +ellps=GRS80 +no_defs"
xBreaks <- seq(138.65, 138.8, by = 0.05)
xLabs <- parse(text = paste(xBreaks, "*degree ~ E", sep = ""))
yBreaks <- seq(-31.2, -31.32, by = -0.04)
yLabs <- parse(text = paste(-yBreaks, "*degree ~ S", sep = ""))
leftN <- tibble(x = c(138.7965, 138.8, 138.8) - 0.01,
                    y = c(-31.2, -31.198, -31.193))
rightN <- tibble(x = c(138.8, 138.8, 138.8035) - 0.01,
                     y = c(-31.193, -31.198, -31.2))
ggMap <- get_map(loc, zoom = 12, maptype = "terrain", color = "bw")
zoomPlot <- ggmap(ggMap, extent = "normal", maprange = FALSE) +
    geom_path(
        aes(long, lat, group = group), 
        data = subset(roads, SURFACE == "UNSE"), 
        linetype = 2, size = 0.3) + 
    geom_path(
        aes(long, lat, group = group), 
        data = subset(roads, SURFACE != "UNSE"), 
        linetype = 1, size = 0.4) +
    geom_label(x = 138.74, y = -31.29, label = "Flinders Ranges NP", alpha = 0.4) +
    geom_label(x = 138.72, y = -31.22, label = "Gum Creek", alpha = 0.4) +
    geom_point(aes(x, y), data = as.data.frame(gc), shape = 3, size = 3) +
    geom_text(aes(x, y), label = "HS", data = as.data.frame(gc), nudge_y = 0.005) +
    geom_polygon(
        data = subset(saPoly, F_CODE == "HD"),
        aes(long, lat, group = group),
        fill = rgb(1, 1, 1, 0), colour = "grey10", size = 0.3) +
    geom_polygon(
        data = subset(saPoly, F_CODE == "PARK"),
        aes(long, lat, group = group),
        fill = rgb(1, 1, 1, 0), colour = "grey10", size = 0.2) +
    geom_point(
        data= filter(pca4Plot, grepl("2012", Population)),
        aes(Longitude, Latitude, colour = Population),
        size = 0.9*ps) +
    geom_polygon(aes(x, y), data = leftN, fill = "white", colour = "grey10", size = 0.4) +
    geom_polygon(aes(x, y), data = rightN, fill = "grey10", colour = "grey10", size = 0.4) +
    geom_text(x = 138.79, y = -31.19, label = "N", 
              colour = "grey10", size = 4) +
    scale_colour_manual(values = popCols[2:3]) +
    coord_cartesian(xlim = c(138.618, 138.81),
                    ylim = c(-31.335, -31.18),
                    expand = 0) +
    scale_x_continuous(breaks = xBreaks, labels = xLabs) +
    scale_y_continuous(breaks = yBreaks, labels = yLabs) +
    guides(colour = FALSE) +
    ggsn::scalebar(x.min = 138.618, x.max = 138.81,
                   y.min = -31.335, y.max = -31.18,
                   transform = TRUE,
                   dist = 2, dist_unit = "km",
                   model = 'GRS80',
                   height = 0.012, st.size = 4,
                   location = 'bottomright',
                   anchor = c(x = 138.8, y = -31.328)) +
    labs(x = "Longitude",
         y = "Latitude") +
    theme(text = element_text(size = fs),
          plot.margin = unit(c(1, 1, 1, 1), "mm"))
ausPolygon <- readRDS(file.path("..", "external", "ausPolygon.RDS"))
ausPts <- SpatialPoints(cbind(x = loc[1], y = loc[2]))
proj4string(ausPts) <- proj4string(ausPolygon)
ausPlot <- ggplot() +
  geom_polygon(data = ausPolygon, 
               aes(long, lat, group = group), fill = "white", colour = "black") + 
  geom_point(data = as.data.frame(ausPts), aes(x, y), size = 1.5) +
  theme_void() +
  theme(plot.background = element_rect(fill = "white", colour = "black"))
## png 
##   2
*Figure 1: Collection points for all 2012 samples with colours showing sub-populations initially defined by PCA analysis and *k*-means clustering.*

Figure 1: Collection points for all 2012 samples with colours showing sub-populations initially defined by PCA analysis and k-means clustering.

zoomLoc <- c(138.753, -31.242)
xBreaks <- seq(138.74, 138.76, by = 0.01)
xLabs <- parse(text = paste(xBreaks, "*degree ~ W", sep = ""))
yBreaks <- seq(-31.235, -31.25, by = -0.005)
yLabs <- parse(text = paste(-yBreaks, "*degree ~ S", sep = ""))
central <- rbind(x = c(138.749, 138.755),
                 y = c(-31.2365, -31.2495)) %>%
  set_colnames(c("min", "max"))
leftN <- tibble(x = c(138.7595, 138.76, 138.76),
                    y = c(-31.234, -31.2335, -31.2325))
rightN <- tibble(x = c(138.76, 138.76, 138.7605),
                     y = c(-31.2325, -31.2335, -31.234))
get_map(zoomLoc, zoom = 15, maptype = "terrain", source = "google", color = "bw") %>%
    ggmap() +
    geom_jitter(
        data = filter(pca4Plot, grepl("2012", Population)), 
        aes(Longitude, Latitude, colour = Population), 
        size = 3, width = 0.0005, height = 0) +
    geom_rect(
        xmin = central["x", "min"],
        xmax = central["x", "max"],
        ymin = central["y", "min"],
        ymax = central["y", "max"],
        fill = "red", alpha = 0.01, colour = "black") +
    geom_polygon(aes(x, y), data = leftN, fill = "white", colour = "grey10", size = 0.4) +
    geom_polygon(aes(x, y), data = rightN, fill = "grey10", colour = "grey10", size = 0.4) +
    geom_text(x = 138.76, y = -31.232, label = "N", 
              colour = "grey10", size = 5) +
    scale_colour_manual(values = popCols[2:3]) +
    scale_x_continuous(breaks = xBreaks, labels = xLabs) +
    scale_y_continuous(breaks = yBreaks, labels = yLabs) +
    theme_bw() +
    guides(colour = FALSE) +
    labs(x = "Longitude",
         y = "Latitude") +
    coord_cartesian(xlim = c(138.74, 138.762),
                    ylim = c(-31.253, -31.231),
                    expand = 0) +
    ggsn::scalebar(
        x.min = 138.74, x.max = 138.762, 
        y.min = -31.253, y.max = -31.231,
        transform = TRUE,
        dist = 0.25, dist_unit = "km",, model = 'GRS80', 
        height = 0.012, st.size = 4,
        location = 'bottomright',
        anchor = c(x = 138.761, y = -31.252)
    )
*Zoomed-in view of the central region for 2012 samples with colours showing sub-populations defined by PCA analysis. The region considered to be the Central Region is shaded in red. Due to overlapping GPS points a small amount of jitter has been added to the x-axis.*

Zoomed-in view of the central region for 2012 samples with colours showing sub-populations defined by PCA analysis. The region considered to be the Central Region is shaded in red. Due to overlapping GPS points a small amount of jitter has been added to the x-axis.

Region Analysis

Removal of SNPs Associated with Collection Region

The structure observed within the 2012 population in the PCA could possibly be explained by recent migration into this region. As the samples collected in the outer regions appeared very similar to the 1996 population in the above plots, this would possibly indicate migration a very recent event as the genetic influence of this has not spread through the wider area. Although this may be due to other factors such as sampling bias, this structure was addressed by identifying SNPs which showed an association with the sub-populations identified by PCA analysis. In this way, any candidate SNPs obtained below will be less impacted by this structure, and will be more reflective of the intended variable under study, i.e. selection over time, as opposed to any internal structure of the 2012 population.

oraRegions <- pca4Plot %>% 
  filter(grepl("2012", Population)) %>% 
  rowwise() %>%
  mutate(yCentral = cut(Latitude, breaks = central["y",], include.lowest = TRUE), 
         xCentral = cut(Longitude, breaks = central["x",], include.lowest = TRUE),
         Central = (is.na(yCentral) + is.na(xCentral)) == 0) %>%
  dplyr::select(Sample, Central) 

Testing for Structure in 2012

This model tests:
H0: No association between genotypes and collection region
HA: An association exists between genotypes and collection region

regionResults <- genotypes %>%
    filter(Population == 2012) %>%
    split(f = .$variant.id) %>%
    mclapply(function(x){
        ft <- list(p.value = NA)
        if (length(unique(x$Genotype)) > 1) {
            ft <- x %>%
                left_join(oraRegions) %>%
                group_by(Genotype, Central) %>%
                tally() %>%
                spread(Genotype, n, fill = 0) %>%
                column_to_rownames("Central") %>%
                fisher.test()
        }
        x %>%
            distinct(variant.id, snpID, chromosome, position) %>%
            mutate(p = ft$p.value)
    }, mc.cores = mc) %>%
    bind_rows() %>%
    filter(!is.na(p)) %>%
    arrange(p)

A total of 1682 SNPs were detected as showing a significant association between genotype and the collection region. Under H0, the number expected using α = 0.05 would be 943, and as this number was approximately double that expected, this was taken as evidence of this being a genuine point of concern for this dataset.

Notably, Type II errors were of principle concern in this instance, and as such every SNP with p < 0.05 in the above test was excluded from downstream analysis.

regionSNPs <- filter(regionResults, p < 0.05)
saveRDS(regionSNPs, "regionSNPs.RDS")

Under this additional filtering step, the original set of 18784 SNPs will be reduced to 17,102 for testing by genotype and allele frequency.

Verification Of Removal

In order to verify that the removal of the above SNPs removed the undesired population structure from the 2012 population, the above PCA was repeated, excluding the SNPs marked for removal. The previous structure noted in the data was no longer evident, and as such, these SNPs were marked for removal during analysis by genotype and allele frequency.

pcaPost <- genotypes %>%
    filter(variant.id %in% snp4PCA$variant.id,
           !variant.id %in% regionSNPs$variant.id) %>%
    dplyr::select(variant.id, Sample, Genotype) %>%
    spread(Sample, Genotype) %>%
    as.data.frame() %>%
    column_to_rownames("variant.id") %>%
    as.matrix() %>%
    apply(2, function(x){
        x[is.na(x)] <- mean(x, na.rm = TRUE)
        x
    }) %>%
    t() %>%
    .[, apply(., 2, function(x){length(unique(x)) > 1})] %>%
    prcomp( center = TRUE) 
pcaPost4Plot <- pcaPost$x %>%
    as.data.frame() %>%
    rownames_to_column("Sample") %>%
    as_tibble() %>%
    dplyr::select(Sample, PC1, PC2, PC3) %>%
    left_join(sampleID) %>%
    mutate(Cluster = kmeans(cbind(PC1, PC2, PC3), 3)$cluster) %>%
    group_by(Cluster) %>%
    mutate(maxY = max(Latitude, na.rm = TRUE)) %>%
    ungroup() %>%
    mutate(Population = case_when(
        Population == 1996 ~ "1996",
        Population == 2010 ~ "Outgroup (Turretfield)",
        maxY == max(maxY) ~ "2012 (Outer)",
        maxY != max(maxY) ~ "2012 (Central)"
    )) %>%
    left_join(genotypes %>% 
                  filter(variant.id %in% snp4PCA$variant.id,
                         !Sample %in% lowCall) %>% 
                  group_by(Sample) %>% 
                  tally() %>% 
                  mutate(imputationRate = 1 - n / nrow(snp4PCA))) 
*Figure 2: Principal Components Analysis showing structures before removal of SNPs denoting collection region in the 2012 population, and after removal of these SNPs*

Figure 2: Principal Components Analysis showing structures before removal of SNPs denoting collection region in the 2012 population, and after removal of these SNPs

SNP Analysis

Genotype Frequency Model

This model tests:
H0: No association between genotypes and populations
HA: An association exists between genotypes and populations

genotypeResults <- genotypes %>%
    filter(Population != 2010,
           !variant.id %in% regionSNPs$variant.id) %>%
    group_by(variant.id, snpID, Population, Genotype) %>%
    tally() %>%
    ungroup() %>%
    split(f = .$variant.id) %>%
    mclapply(function(x){
        ft <- list(p.value = NA)
        if (length(unique(x$Genotype)) > 1) {
            ft <- x %>%
                spread(Genotype, n, fill = 0) %>%
                column_to_rownames("Population") %>%
                dplyr::select(-variant.id, -snpID) %>%
                fisher.test()
        }
        x %>% 
            distinct(variant.id) %>%
            mutate(p = ft$p.value)
    },mc.cores = mc) %>%
    bind_rows() %>%
    filter(!is.na(p)) %>%
    mutate(FDR = p.adjust(p, "fdr"),
           adjP = p.adjust(p, "bonferroni")) %>%
    arrange(p) %>%
    left_join(genotypes %>%
                  distinct(variant.id, snpID, chromosome, position)) %>%
    dplyr::select(variant.id, snpID, chromosome, position, everything())

Under the full genotype model:

  • 15 genotypes were detected as being significantly associated with the two populations when controlling the FWER at α = 0.05
  • 38 genotypes were detected as being significantly associated with the two populations when controlling the FDR at α = 0.05
  • For the most highly ranked SNP (3391201_92), the minor allele has been completely lost in the 2012 population
SNPs with adjusted p-values < 0.05 when analysing by genotype.
snpID chromosome position p FDR adjP Gene within 100kb
3391201_92 GL018705 1,937,359 6.103e-08 0.0009035 0.001047
686773_29 3 90,851,512 1.053e-07 0.0009035 0.001807 CRISPLD1
686773_29 3 90,851,512 1.053e-07 0.0009035 0.001807
916731_91 4 89,602,973 1.865e-07 0.0009837 0.003201 RIC8B
916731_91 4 89,602,973 1.865e-07 0.0009837 0.003201 RFX4
836950_151 4 35,111,855 2.587e-07 0.0009837 0.00444 TMPRSS12
836950_151 4 35,111,855 2.587e-07 0.0009837 0.00444 METTL7A
836950_151 4 35,111,855 2.587e-07 0.0009837 0.00444 SLC11A2
321186_117 2 2.8e+07 2.866e-07 0.0009837 0.004919 KLF3
2227006_109 13 129,650,867 4.419e-07 0.001051 0.007583 ID3
2227006_109 13 129,650,867 4.419e-07 0.001051 0.007583 E2F2
2227006_109 13 129,650,867 4.419e-07 0.001051 0.007583 ASAP3
2227006_109 13 129,650,867 4.419e-07 0.001051 0.007583 TCEA3
2227006_109 13 129,650,867 4.419e-07 0.001051 0.007583
3040789_114 19 30,689,426 4.838e-07 0.001051 0.008303 MSI2
4165193_106 GL019119 147,630 4.901e-07 0.001051 0.008411
4223149_99 GL019332 5,745 6.122e-07 0.001167 0.01051
1089925_112 7 35,584,375 9.805e-07 0.001583 0.01683
464226_128 2 125,603,458 1.014e-06 0.001583 0.01741 BCL11A
3374849_109 GL018704 1,294,009 1.387e-06 0.001984 0.02381
3695415_108 GL018751 737,318 1.837e-06 0.002395 0.03153 TRAF3
3695415_108 GL018751 737,318 1.837e-06 0.002395 0.03153 RCOR1
2099634_108 13 45,086,624 2.028e-06 0.002395 0.0348 WARS2
2099634_108 13 45,086,624 2.028e-06 0.002395 0.0348 TBX15
1902686_98 12 60,113,885 2.094e-06 0.002395 0.03593
1902686_98 12 60,113,885 2.094e-06 0.002395 0.03593
1902686_98 12 60,113,885 2.094e-06 0.002395 0.03593
1902686_98 12 60,113,885 2.094e-06 0.002395 0.03593 DPPA5
1902686_98 12 60,113,885 2.094e-06 0.002395 0.03593
1902686_98 12 60,113,885 2.094e-06 0.002395 0.03593
1902686_98 12 60,113,885 2.094e-06 0.002395 0.03593
1902686_98 12 60,113,885 2.094e-06 0.002395 0.03593
Figure 3a: Manhattan plot showing results for all SNPs on chromosomes 1:21 for analysis by genotype. The horizontal line indicates the cutoff for an FDR of 5%, with SNPs with an adjusted p-value < 0,05 shown in green

Figure 3a: Manhattan plot showing results for all SNPs on chromosomes 1:21 for analysis by genotype. The horizontal line indicates the cutoff for an FDR of 5%, with SNPs with an adjusted p-value < 0,05 shown in green

*Variants detected as significant to an FDR of 5%. Those denoted with an asterisk received a Bonferroni-adjusted p-value < 0.05 and these typically involved reduction of the minor allele frequency and a shift towards homozygous reference. The remaining sites showed a combination of the same and increasing minor allele abundance, with some variants becoming exclusively heterozygous in the 2012 population.*

Variants detected as significant to an FDR of 5%. Those denoted with an asterisk received a Bonferroni-adjusted p-value < 0.05 and these typically involved reduction of the minor allele frequency and a shift towards homozygous reference. The remaining sites showed a combination of the same and increasing minor allele abundance, with some variants becoming exclusively heterozygous in the 2012 population.

Allele Frequency Model

This model tests:
H0: No association between allele frequencies and populations
HA: An association exists between allele frequencies and populations

alleleResults <- genotypes %>%
    filter(Population != 2010,
           !variant.id %in% regionSNPs$variant.id) %>%
    group_by(snpID, Population) %>%
    summarise(P = sum(2 - Genotype),
              Q = sum(Genotype)) %>%
    ungroup() %>%
    split(f = .$snpID) %>%
    mclapply(function(x){
        m <- as.matrix(x[c("P", "Q")])
        ft <- list(p.value = NA) 
        if (length(m) == 4) {
            ft <- fisher.test(m)
        }
        x %>%
            mutate(MAF = Q / (P + Q)) %>%
            dplyr::select(snpID, Population, MAF) %>%
            spread(Population, MAF) %>%
            mutate(p = ft$p.value)
    },mc.cores = mc) %>%
    bind_rows() %>%
    filter(!is.na(p)) %>%
    dplyr::rename(MAF_1996 = `1996`,
                  MAF_2012 = `2012`) %>%
    mutate(FDR = p.adjust(p, "fdr"),
           adjP = p.adjust(p, "bonferroni")) %>%
    arrange(p) %>%
    left_join(genotypes %>%
                  distinct(snpID, chromosome, position)) %>%
    dplyr::select(snpID, chromosome, position, everything())

Under this model:

  • 5 SNP alleles were detected as being significantly associated with the two populations when controlling the FWER at α = 0.05. However, as these SNPs were within 21nt of each other, this may represent the same haplotype
  • 38 SNP alleles were detected as being significantly associated with the two populations when controlling the FDR at α = 0.05
SNPs considered as significant when analysing by genotype using an FDR cutoff of 0.05
snpID chromosome position MAF_1996 MAF_2012 p FDR adjP Gene within 100kb
3391201_92 GL018705 1,937,359 0.2255 0.009615 3.44e-07 0.005911 0.005916
916731_91 4 89,602,973 0.1961 0 6.875e-07 0.005911 0.01182 RIC8B
916731_91 4 89,602,973 0.1961 0 6.875e-07 0.005911 0.01182 RFX4
3040789_114 19 30,689,426 0.1863 0 1.531e-06 0.007628 0.02632 MSI2
321186_117 2 2.8e+07 0.25 0.0122 1.866e-06 0.007628 0.03209 KLF3
1009106_1 6 9,037,073 0.4432 0.1277 2.52e-06 0.007628 0.04333 SMG1
1009106_1 6 9,037,073 0.4432 0.1277 2.52e-06 0.007628 0.04333 ARL6IP1
836950_151 4 35,111,855 0.2907 0.04082 3.086e-06 0.007628 0.05307 TMPRSS12
836950_151 4 35,111,855 0.2907 0.04082 3.086e-06 0.007628 0.05307 METTL7A
836950_151 4 35,111,855 0.2907 0.04082 3.086e-06 0.007628 0.05307 SLC11A2
464226_128 2 125,603,458 0.1915 0 3.105e-06 0.007628 0.0534 BCL11A
2227006_109 13 129,650,867 0.2708 0.03333 4.172e-06 0.008366 0.07174 ID3
2227006_109 13 129,650,867 0.2708 0.03333 4.172e-06 0.008366 0.07174 E2F2
2227006_109 13 129,650,867 0.2708 0.03333 4.172e-06 0.008366 0.07174 ASAP3
2227006_109 13 129,650,867 0.2708 0.03333 4.172e-06 0.008366 0.07174 TCEA3
2227006_109 13 129,650,867 0.2708 0.03333 4.172e-06 0.008366 0.07174
1902686_98 12 60,113,885 0.1633 0 4.749e-06 0.008366 0.08166
1902686_98 12 60,113,885 0.1633 0 4.749e-06 0.008366 0.08166
1902686_98 12 60,113,885 0.1633 0 4.749e-06 0.008366 0.08166
1902686_98 12 60,113,885 0.1633 0 4.749e-06 0.008366 0.08166 DPPA5
1902686_98 12 60,113,885 0.1633 0 4.749e-06 0.008366 0.08166
1902686_98 12 60,113,885 0.1633 0 4.749e-06 0.008366 0.08166
1902686_98 12 60,113,885 0.1633 0 4.749e-06 0.008366 0.08166
1902686_98 12 60,113,885 0.1633 0 4.749e-06 0.008366 0.08166
3374849_109 GL018704 1,294,009 0.2065 0.0102 4.865e-06 0.008366 0.08366
4165193_106 GL019119 147,630 0.2736 0.04167 5.576e-06 0.008717 0.09589
4044777_119 GL018933 204,058 0.01754 0.2019 7.749e-06 0.0111 0.1333 FNBP1
4044777_119 GL018933 204,058 0.01754 0.2019 7.749e-06 0.0111 0.1333 USP20
4044777_119 GL018933 204,058 0.01754 0.2019 7.749e-06 0.0111 0.1333 C9orf78
4044777_119 GL018933 204,058 0.01754 0.2019 7.749e-06 0.0111 0.1333 TOR1A
4044777_119 GL018933 204,058 0.01754 0.2019 7.749e-06 0.0111 0.1333 TOR1B
4044777_119 GL018933 204,058 0.01754 0.2019 7.749e-06 0.0111 0.1333 PTGES
4044777_119 GL018933 204,058 0.01754 0.2019 7.749e-06 0.0111 0.1333 PRRX2
1089925_112 7 35,584,375 0.2841 0.04348 9.725e-06 0.01286 0.1672
3695415_108 GL018751 737,318 0.2609 0.03261 1.275e-05 0.01447 0.2193 TRAF3
3695415_108 GL018751 737,318 0.2609 0.03261 1.275e-05 0.01447 0.2193 RCOR1
4223149_99 GL019332 5,745 0.3077 0.05814 1.314e-05 0.01447 0.226
2099634_108 13 45,086,624 0.2609 0.03409 1.412e-05 0.01447 0.2429 WARS2
2099634_108 13 45,086,624 0.2609 0.03409 1.412e-05 0.01447 0.2429 TBX15
87917_119 1 64,635,286 0.186 0 1.43e-05 0.01447 0.2459 CEP78
87917_119 1 64,635,286 0.186 0 1.43e-05 0.01447 0.2459 PSAT1
4010189_98 GL018907 283,766 0.1932 0.01 1.627e-05 0.01495 0.2798 BRWD1
4010189_98 GL018907 283,766 0.1932 0.01 1.627e-05 0.01495 0.2798 LCA5L
4010189_98 GL018907 283,766 0.1932 0.01 1.627e-05 0.01495 0.2798
2689075_49 16 56,527,308 0.05814 0.3043 1.652e-05 0.01495 0.284 ESRRG
4149102_107 GL019077 34,265 0.2232 0.02941 2.279e-05 0.01959 0.3919 IFT140
4149102_107 GL019077 34,265 0.2232 0.02941 2.279e-05 0.01959 0.3919 TMEM204
4149102_107 GL019077 34,265 0.2232 0.02941 2.279e-05 0.01959 0.3919 CRAMP1
4149102_107 GL019077 34,265 0.2232 0.02941 2.279e-05 0.01959 0.3919
1997286_93 12 141,844,290 0.08889 0.3556 2.435e-05 0.01987 0.4187 ESR1
2906428_79 18 25,311,176 0.1731 0.4434 2.542e-05 0.01987 0.4371 CDK1
2405359_66 14 97,813,263 0.02083 0.2021 4.48e-05 0.0335 0.7704 STXBP5L
3000704_65 19 13,055,154 0.03191 0.234 5.416e-05 0.03776 0.9314
4146664_90 GL019084 74,038 0.1961 0.02041 5.49e-05 0.03776 0.9441 GNB1
4146664_90 GL019084 74,038 0.1961 0.02041 5.49e-05 0.03776 0.9441 NADK
4146664_90 GL019084 74,038 0.1961 0.02041 5.49e-05 0.03776 0.9441
4098854_101 GL018985 129,495 0.1702 0.01042 6.686e-05 0.04259 1
4098854_101 GL018985 129,495 0.1702 0.01042 6.686e-05 0.04259 1 UFD1
4098854_101 GL018985 129,495 0.1702 0.01042 6.686e-05 0.04259 1 C22orf39
4098854_101 GL018985 129,495 0.1702 0.01042 6.686e-05 0.04259 1 MRPL40
4098854_101 GL018985 129,495 0.1702 0.01042 6.686e-05 0.04259 1 HIRA
4098838_14 GL018985 123,328 0.3636 0.1275 6.797e-05 0.04259 1
4098838_14 GL018985 123,328 0.3636 0.1275 6.797e-05 0.04259 1 UFD1
4098838_14 GL018985 123,328 0.3636 0.1275 6.797e-05 0.04259 1 C22orf39
4098838_14 GL018985 123,328 0.3636 0.1275 6.797e-05 0.04259 1 MRPL40
4098838_14 GL018985 123,328 0.3636 0.1275 6.797e-05 0.04259 1 HIRA
1882774_17 12 40,509,779 0.3214 0.5943 7.288e-05 0.04259 1
1045185_75 7 2,702,919 0.2143 0.03774 7.807e-05 0.04259 1
1045185_75 7 2,702,919 0.2143 0.03774 7.807e-05 0.04259 1 ZNF212
1045185_75 7 2,702,919 0.2143 0.03774 7.807e-05 0.04259 1 ZNF282
1045185_75 7 2,702,919 0.2143 0.03774 7.807e-05 0.04259 1 ZNF398
3016258_99 19 19,178,148 0.22 0.03261 7.861e-05 0.04259 1 SEZ6
3016258_99 19 19,178,148 0.22 0.03261 7.861e-05 0.04259 1 PHF12
3016258_99 19 19,178,148 0.22 0.03261 7.861e-05 0.04259 1 DHRS13
3016258_99 19 19,178,148 0.22 0.03261 7.861e-05 0.04259 1 FLOT2
3016258_99 19 19,178,148 0.22 0.03261 7.861e-05 0.04259 1 ERAL1
3016258_99 19 19,178,148 0.22 0.03261 7.861e-05 0.04259 1 FAM222B
2028473_73 13 2,979,383 0.01818 0.1731 8.051e-05 0.04259 1 COP1
3829050_109 GL018791 957,960 0.163 0.009615 8.094e-05 0.04259 1 FHAD1
3829050_109 GL018791 957,960 0.163 0.009615 8.094e-05 0.04259 1 TMEM51
2580149_42 15 87,473,844 0.3511 0.1154 8.416e-05 0.04259 1 ADGRL3
2527760_104 15 43,156,117 0.1531 0.4057 8.594e-05 0.04259 1 GIMD1
2527760_104 15 43,156,117 0.1531 0.4057 8.594e-05 0.04259 1 AIMP1
2527760_104 15 43,156,117 0.1531 0.4057 8.594e-05 0.04259 1 TBCK
3999128_75 GL018883 283,912 0.3654 0.125 8.683e-05 0.04259 1 SERPINA6
3999128_75 GL018883 283,912 0.3654 0.125 8.683e-05 0.04259 1
3999128_75 GL018883 283,912 0.3654 0.125 8.683e-05 0.04259 1
3999128_75 GL018883 283,912 0.3654 0.125 8.683e-05 0.04259 1
3999128_75 GL018883 283,912 0.3654 0.125 8.683e-05 0.04259 1
3999128_75 GL018883 283,912 0.3654 0.125 8.683e-05 0.04259 1
3999128_75 GL018883 283,912 0.3654 0.125 8.683e-05 0.04259 1 SERPINA11
3999128_75 GL018883 283,912 0.3654 0.125 8.683e-05 0.04259 1 SERPINA9
3999128_75 GL018883 283,912 0.3654 0.125 8.683e-05 0.04259 1 SERPINA12
4176850_23 GL019154 883 0.234 0.04082 8.916e-05 0.04259 1 MTHFSD
4176850_23 GL019154 883 0.234 0.04082 8.916e-05 0.04259 1 FOXF1
686773_29 3 90,851,512 0.4375 0.1667 9.731e-05 0.04522 1 CRISPLD1
686773_29 3 90,851,512 0.4375 0.1667 9.731e-05 0.04522 1
3032107_92 19 26,020,372 0.1939 0.02083 0.0001019 0.04612 1
## png 
##   2
Manhattan plot showing results for all SNPs on chromosomes 1:21 when analysing by allele frequencies. The horizontal line indicates the cutoff for an FDR of 5%, with SNPs considered significant under the Bonferroni adjustment shown in green.

Manhattan plot showing results for all SNPs on chromosomes 1:21 when analysing by allele frequencies. The horizontal line indicates the cutoff for an FDR of 5%, with SNPs considered significant under the Bonferroni adjustment shown in green.

*Comparison of minor allele frequencies in both populations. SNPs with an FDR-adjusted p-value < 0.05 are coloured red, whilst those with a Bonferroni-adjusted p-value are additionally labelled.*

Comparison of minor allele frequencies in both populations. SNPs with an FDR-adjusted p-value < 0.05 are coloured red, whilst those with a Bonferroni-adjusted p-value are additionally labelled.

Analysis Using the FLK model

This was performed seperately and no SNPs of specific interest were detected.

Export of Data for Bayescan

A VCF was required with only the 1996 and 2012 populations, and restricted to the candidate SNPs after pruning for linkage disequilibrium and detection of the allele in the 1996 population.

gdsFile <- seqOpen(gdsPath, readonly = TRUE)
seqSetFilter(gdsFile, 
             variant.id = genotypes %>% 
                 distinct(variant.id, snpID) %>%
                 filter(snpID %in% filter(regionResults, p > 0.05)$snpID) %>% 
                 .[["variant.id"]],
             sample.id = sampleID %>% 
                 filter(Population %in% c(1996, 2012)) %>% 
                 .[["Sample"]])
seqGDS2VCF(gdsFile, "../5_stacks/vcf/filtered.vcf.gz")
seqResetFilter(gdsFile)
seqClose(gdsFile)

Session Information

R version 3.6.0 (2019-04-26)

Platform: x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_AU.UTF-8, LC_NUMERIC=C, LC_TIME=en_AU.UTF-8, LC_COLLATE=en_AU.UTF-8, LC_MONETARY=en_AU.UTF-8, LC_MESSAGES=en_AU.UTF-8, LC_PAPER=en_AU.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_AU.UTF-8 and LC_IDENTIFICATION=C

attached base packages: stats4, parallel, grid, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: rtracklayer(v.1.44.0), plyranges(v.1.4.1), GenomicRanges(v.1.36.0), GenomeInfoDb(v.1.20.0), IRanges(v.2.18.1), S4Vectors(v.0.22.0), BiocGenerics(v.0.30.0), ggrepel(v.0.8.1), qqman(v.0.1.4), ggsn(v.0.5.0), rgdal(v.1.4-4), ggmap(v.3.0.0), sp(v.1.3-1), readxl(v.1.3.1), forcats(v.0.4.0), stringr(v.1.4.0), dplyr(v.0.8.1), purrr(v.0.3.2), readr(v.1.3.1), tidyr(v.0.8.3), tibble(v.2.1.3), ggplot2(v.3.1.1), tidyverse(v.1.2.1), magrittr(v.1.5), scales(v.1.0.0), pander(v.0.6.3), SNPRelate(v.1.18.0), SeqArray(v.1.24.0) and gdsfmt(v.1.20.0)

loaded via a namespace (and not attached): nlme(v.3.1-140), matrixStats(v.0.54.0), bitops(v.1.0-6), sf(v.0.7-4), lubridate(v.1.7.4), httr(v.1.4.0), tools(v.3.6.0), backports(v.1.1.4), R6(v.2.4.0), KernSmooth(v.2.23-15), DBI(v.1.0.0), lazyeval(v.0.2.2), colorspace(v.1.4-1), withr(v.2.1.2), tidyselect(v.0.2.5), curl(v.3.3), compiler(v.3.6.0), Biobase(v.2.44.0), cli(v.1.1.0), rvest(v.0.3.4), xml2(v.1.2.0), DelayedArray(v.0.10.0), labeling(v.0.3), classInt(v.0.3-3), Rsamtools(v.2.0.0), digest(v.0.6.19), foreign(v.0.8-71), rmarkdown(v.1.13), XVector(v.0.24.0), jpeg(v.0.1-8), pkgconfig(v.2.0.2), htmltools(v.0.3.6), highr(v.0.8), rlang(v.0.3.4), rstudioapi(v.0.10), generics(v.0.0.2), jsonlite(v.1.6), BiocParallel(v.1.18.0), RCurl(v.1.95-4.12), GenomeInfoDbData(v.1.2.1), Matrix(v.1.2-17), Rcpp(v.1.0.1), munsell(v.0.5.0), stringi(v.1.4.3), yaml(v.2.2.0), SummarizedExperiment(v.1.14.0), zlibbioc(v.1.30.0), plyr(v.1.8.4), maptools(v.0.9-5), crayon(v.1.3.4), lattice(v.0.20-38), Biostrings(v.2.52.0), haven(v.2.1.0), hms(v.0.4.2), knitr(v.1.23), pillar(v.1.4.1), rjson(v.0.2.20), XML(v.3.98-1.20), glue(v.1.3.1), evaluate(v.0.14), calibrate(v.1.7.2), modelr(v.0.1.4), png(v.0.1-7), RgoogleMaps(v.1.4.3), cellranger(v.1.1.0), gtable(v.0.3.0), assertthat(v.0.2.1), xfun(v.0.7), broom(v.0.5.2), e1071(v.1.7-2), class(v.7.3-15), GenomicAlignments(v.1.20.0) and units(v.0.6-3)